Evidence map›Paper›PMID 42351001›Full record

ArticleBMC pediatrics2026

Can ChatGPT provide reliable guidance to families of children with Duchenne Muscular Dystrophy? A dual perspective of parents and experts.

Gülşah Çelik, Hanife Hale Hekim, Meral Bilgilisoy Filiz, Naciye Füsun Toraman, Dilek Yapar

Abstract read
In one paragraph

Article in BMC pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Gülşah ÇelikDepartment of Physical Medicine and Rehabilitation, Faculty of Medicine, Kastamonu University, Kastamonu, Turkey. gulsahberberr@gmail.com.ORCID 0009-0002-1170-4110
Hanife Hale HekimDepartment of Physical Medicine and Rehabilitation, Antalya Training and Research Hospital, University of Health Sciences, Antalya, Turkey.ORCID 0000-0001-6555-2111
Meral Bilgilisoy FilizDepartment of Physical Medicine and Rehabilitation, Antalya Training and Research Hospital, University of Health Sciences, Antalya, Turkey.ORCID 0000-0002-3064-2878
Naciye Füsun ToramanDepartment of Physical Medicine and Rehabilitation, Antalya Training and Research Hospital, University of Health Sciences, Antalya, Turkey.ORCID 0000-0002-8141-4525
Dilek YaparInstitute of Health Science, Medical Informatics, Akdeniz University, Antalya, Turkey.ORCID 0000-0001-7656-1152

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-driven chatbots, such as ChatGPT, have the potential to improve healthcare communication by providing timely and accessible medical information. Duchenne Muscular Dystrophy (DMD) is a progressive neuromuscular disorder that significantly impacts patients and their families. Parents of DMD patients seek reliable information on disease progression, complications, and care management. However, the effectiveness of AI-generated medical guidance remains unclear.

methodsThis study evaluated ChatGPT's capacity to provide guidance to DMD patients' families by analyzing expert and parental assessments. Ten frequently asked questions were compiled from parental inquiries and submitted to ChatGPT-4o. Responses were evaluated by physiatrists, pediatric neurologists, and parents using structured rubrics. Experts rated responses based on Accuracy, Currency, Comprehensiveness, Usefulness, and Understandability, while parents assessed them in terms of Satisfaction, Empathy, Comprehensiveness, and Understandability.

resultsExperts rated Accuracy highest (4.3 ± 0.6; 91.8% of ratings ≥ 4), followed by Usefulness (4.2 ± 0.6; 88.6%), Currency (4.2 ± 0.7; 87.1%), Comprehensiveness (4.2 ± 0.7; 85.1%), and Understandability (4.2 ± 0.7; 83.4%). Parents rated Understandability highest (4.5 ± 0.8; 86.0%), followed by Comprehensiveness (4.4 ± 0.8; 84.0%) and Satisfaction (4.4 ± 0.8; 83.8%). Empathy received the lowest parental score (4.2 ± 0.9; 75.1%). Although both groups generally evaluated the responses favorably (median scores ≥ 4), experts tended to prioritize technical accuracy, whereas parents placed greater emphasis on clarity and practical usefulness.

conclusionChatGPT shows promise as an informational tool for parents of DMD, particularly in providing accurate and applicable medical guidance. However, limitations in empathy suggest a need for refinement. While ChatGPT should complement rather than replace human expertise, future improvements could expand its role in patient education and public health. Further research is needed to optimize AI-driven healthcare communication for reliability and ethical considerations.

Indexed as

Muscular Dystrophy, DuchenneParentsChildFemaleGenerative Artificial IntelligenceHumansMaleChatGPTDuchenne muscular dystrophyExpert evaluationLarge language modelsRubric

Identifiers

PMID42351001
PMCPMC13563781

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.